RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair
Abstract
Automatic program repair (APR) is crucial to reduce manual debugging efforts for developers and improve software reliability. While conventional search-based techniques typically rely on heuristic rules or a redundancy assumption to mine fix patterns, recent years have witnessed the surge of deep learning (DL) based approaches to automate the program repair process in a data-driven manner. However, their performance is often limited by a fixed set of parameters to model the highly complex search space of APR.
BibTeX
@inproceedings{Wang-al:FSE23,
author = {Weishi Wang and
Yue Wang and
Shafiq Joty and
Steven C. H. Hoi},
title = {{RAP-Gen:} {Retrieval-Augmented} Patch Generation with {CodeT5} for Automatic Program Repair},
booktitle = {{ESEC/SIGSOFT} {FSE}},
pages = {146--158},
publisher = {{ACM}},
year = {2023},
}